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ENTrOPY TOLErANT FUZZY C-MEANS IN MEDICAL IMAGES

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Journal of Innovative Optical Health Sciences 2011年第4期4卷 447-462页

作者： S.r.KANNAN S.rAMATHILAGAM r.devi YUEH-MIN HUANG Department of Mathematics ramanujan School of Mathematical Sciences Pondicherry University India Department of Mathematics Periyar Government College CuddaloreIndia Department of Engineering Science National Cheng Kung University TainanTaiwan

Segmenting the Dynamic Contrast-Enhanced Breast Magnetic resonance Images(DCE-BMrI)is an extremely important task to diagnose the disease because it has the highest specificity when acquired with high temporal and spatial resolution and is also corrupted by heavy noise,outliers,and other imaging *** this paper,we intend to develop efficient robust segmentation algorithms based on fuzzy clustering approach for segmenting the *** proposed segmentation algorithms have been amalgamated with effective kernel-induced distance measure on standard fuzzy c-means algorithm along with the spatial neighborhood information,entropy term,and tolerance vector into a fuzzy clustering structure for segmenting the *** significant feature of our proposed algorithms is its capability tofind the optimal membership grades and obtain effective cluster centers automatically by minimizing the proposed robust objective ***,this article demonstrates the superiority of the proposed algorithms for segmenting DCE-BMrI in comparison with other recent kernel-based fuzzy c-means *** the clustering accuracies of the proposed algorithms are validated by using silhouette method in comparison with existed fuzzy clustering algorithms.

关键词： Fuzzy clustering algorithms entropy method segmentation medical images

FUZZY.C-MEANS IN FINDING SUBTYPES OF CANCErS IN CANCEr DATABASE

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Journal of Innovative Optical Health Sciences 2014年第1期7卷 109-128页

作者： S.r.KANNAN S.rAMTHILAGAM r.devi T.P.HONG Department of Mathematics Pondicherry Central University India Department of Mathematics Periyar Government College Tamil Nadu Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan

Finding subtypes of cancer in breast cancer database is an extremely dificult task because ofheavy noise by measurement *** of the recent clustering techniques for breast cancerdatabase to achieve cancerous and noncancerous often weigh down the interpretability of ***,this paper tries to find effective Fuzzy C-Means-based clustering techniques toidentify the proper subtypes of cancer in breast cancer database,This paper obtains the objectivefunction of ffective Fuzzy C-Means clustering techriques by incorporating the kermel induceddist ance function,renyi's entropy function,weighted dist ance measure and neighborhood ternsbased spatial *** efectiveness of the proposed methods are proved through the ex-perimental works on Lung cancer database,IrIS dataset,Wine dat aset,Checkerboard dataset,Time Series dataset and Yeast ***,the proposed methods are implemented suc-cesfully to cluster the breast cancer dat abase into cancerous and *** clusteringaccuracy has been validat ed through error matrix and silhouette method.

关键词： Fuzzy C-Means kenel induced distance entropy terms cancer database

An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

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China Communications 2024年第5期21卷 249-260页

作者： r.rajakumar S.Sathiya devi Srinivasa ramanujan Centre SASTRA Deemed UniversityKumbakonamTamil Nadu 612001India University College of Engineering BIT CampusAnna UniversityTiruchirappalli 620024India

recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,*** recent developments of deep learning(DL)models find helpful in the detection and classification of *** article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)*** aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming *** proposed OS-ODLSDC model initially undergoes preprocessing *** streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling ***,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and ***,the root means square propagation(rMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM *** ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

关键词： anomaly detection deep learning hyperparameter optimization oversampling SMOTE streaming data

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks(SMPISCS)

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Cybersecurity 2019年第1期2卷 76-90页

作者： Kanchana devi V Ganesan r School of Computing Science and Engineering VIT University Chennai CampusChennaiTamilNaduIndia

In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data *** quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control *** performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet *** selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery *** Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the *** model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into *** experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.

关键词： Information security routing overhead Selfish sensor nodes Semi Markov process Wireless sensor networks

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Enhancement of IoT device security using an Improved Elliptic Curve Cryptography algorithm and malware detection utilizing deep LSTM

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High-Confidence Computing 2023年第2期3卷 18-31页

作者： r.Aiyshwariya devi A.r.Arunachalam Department of Computer Science and Engineering Dr.M.G.R.Educational and Research InstituteChennai 600095India

Internet of things(IoT)has become more popular due to the development and potential of smart technology *** concerns against IoT infrastructure,applications,and devices have grown along with the need for IoT *** system security protocols are difficult due to the diverse capabilities of IoT devices and the dynamic,ever-changing environment,and simply applying basic security requirements is ***,this proposed work designs a malware detection and prevention approach for secure data transmission among IoT *** malware detection approach is designed with the aid of a deep learning *** initial process is identifying attack nodes from normal nodes through a trust value using contextual *** discovering attack nodes,these are considered for predicting different kinds of attacks present in the network,while some preprocessing and feature extraction strategies are applied for effective *** Deep LSTM classifier is applied for this malware detection *** completed malware detection,prevention is performed with the help of the Improved Elliptic Curve Cryptography(IECC)algorithm.A hybrid MA-BW optimization is adopted for selecting the optimal key during *** 3.8 software is used to test the performance of the proposed approach,and several existing techniques are considered to evaluate its *** proposed approach obtained 95%of accuracy,5%of error value and 92%of *** addition,the improved ECC algorithm is also compared with some existing algorithm which takes 6.02 s of execution *** to the other methods,the proposed approach provides better security to IoT gadgets during data transmission.

关键词： Deep LSTM Improved Elliptic Curve Cryptography Malware detection Prediction of different kinds of attacks IoT gadgets

Energy-Aware Scheduling for Tasks with Target-Time in Blockchain based Data Centres

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Computer Systems Science & Engineering 2022年第2期40卷 405-419页

作者： I.devi G.r.Karpagam Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004India

Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use *** extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers(DC)leads to massive power *** intensifying scale of DCs has made energy consumption a critical *** paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data ***,an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy *** estab-lished model was analyzed with a target-time responsive precedence scheduling *** observations were analyzed and compared with the traditional scheduling *** outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy *** investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.

关键词： Cloud computing taskscheduling blockchain energy-aware virtual machine

Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

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Computer Systems Science & Engineering 2023年第3期44卷 1897-1910页

作者： r.Vijay Anandh S.rukmani devi Department of Electronics and Communication Engineering RMK College of Engineering and TechnologyTiruvallurTamilnadu601206India Department of Electronics and Communication Engineering RMD Engineering CollegeGummidipundiTamilnadu601206India

The demand for the exploration of ocean resources is increasing *** image data plays a significant role in many research *** this,the visual quality of underwater images is degraded because of two main factors namely,backscattering and ***,visual enhancement has become an essential process to recover the required data from the *** algorithms had been proposed in a decade for improving the quality of *** paper aims to propose a single image enhancement technique without the use of any external *** that,the degraded images are subjected to two main processes namely,color correction and image ***,veiling light and transmission light is estimated tofind the color required for *** light refers to unwanted light,whereas transmission light refers to the required light for color *** estimated outputs are applied in the scene recovery *** image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement *** resultants are divided into three weight maps namely,luminance,saliency,chromaticity and fused using the Laplacian *** results obtained are graphically compared with their input data using rGB Histogram ***,image quality is measured and tabulated using underwater image quality measures.

关键词： Underwater image backscattering attenuation image fusion veiling light white balance laplacian pyramid

Loss of Exchange Symmetry in Multiqubit States under Ising Chain Evolution

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Chinese Physics Letters 2011年第2期28卷 39-42页

作者： Sudha B.G.Divyamani A.r.Usha devi Department of Physics Kuvempu UniversityShankaraghattaShimoga-577451India DAMTP Centre for Mathematical SciencesWilberforce RoadCambridgeCB3OWAUK Department of Physics Bangalore UniversityBangalore-560056India Inspire Institute Inc. McLeanVA 22101USA.

Keeping in view of importance of exchange symmetry aspects in studies on spin squeezing of multiqubit states,we show that the one-dimensional Ising Hamiltonian with nearest neighbor interactions does not retain the exchange symmetry of initially symmetric multiqubit *** we show that among 4-qubit states obeying exchange symmetry,all states except W class(and their linear combination)lose their symmetry under time evolution with Ising *** the loss of symmetry of the initially symmetric states to rotational asymmetry of the one-dimensional Ising Hamiltonian with more than 3 qubits,we indicate that all N-qubit states(N>5)obeying permutation symmetry lose their symmetry after time evolution with Ising Hamiltonian.

关键词： symmetry Ising qubit

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2101-2116页

作者： r.rajakumar S.Sathiya devi Anna University Chennai600025India University College of Engineering BIT CampusAnna UniversityTiruchirappalli620024India

Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor *** investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classiﬁcation problems have received consid-erable *** the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes *** study develops an outlier detec-tion with feature selection technique for streaming data classiﬁcation,named ODFST-SDC ***,streaming data is pre-processed in two ways namely categorical encoding and null value *** addition,Local Correla-tion Integral(LOCI)is used which is signiﬁcant in the detection and removal of ***,red deer algorithm(rDA)based FS approach is employed to derive an optimal subset of ***,kernel extreme learning machine(KELM)classiﬁer is used for streaming data classiﬁ*** design of LOCI based outlier detection and rDA based FS shows the novelty of the *** order to assess the classiﬁcation outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark *** experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.

关键词： Streaming data classiﬁcation outlier removal feature selection machine learning metaheuristics